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A Framework for Understanding
Unintended consequences of
Machine Learning
Author: Harini Suresh (MIT), John V. Guttag(MIT)
Presented: Chenguang Xu “Shine”
The Problem with Biased data
• Various unwanted consequences of ML algorithm arise in
some way from biased data.
• Bias refers to an unintended or potentially harmful
property of the data.

• Data is a product of many factors, and is the product of a
process
An illustrative Scenario
Lack of data on women, introducing
more data solved the issue.
The use of a proxy label (human assessment of
quality) versus the true label (actual qualification)
allowed the model to discriminate by gender.
Five Sources of Bias in ML
Historical Bias
It is a fundamental, structural issue with the
very first step of the data generation process.
Representation Bias
• It arises when defining and sampling from a population. 

• It can arise for several reasons:
• The sampling methods only reach a portion of the
population.

• The population of interest has changed or is distinct
from the population used during model training.
Representation Bias (cont.)
Shankar, Shreya, et al. "No classification without representation: Assessing geodiversity issues in open
data sets for the developing world." arXiv preprint arXiv:1711.08536 (2017).
Representation Bias (cont.)
Photos of bridegrooms from
different countries aligned by the
log-likelihood that the classifier
trained on Open Images assigns to
the bridegroom class. 

Shankar, Shreya, et al. "No classification without representation: Assessing geodiversity issues in open
data sets for the developing world." arXiv preprint arXiv:1711.08536 (2017).
Measurement Bias
• It arises when subsequently choosing and measuring the
particular features of interest.

• It can arise in several ways:
• The granularity of data varies across groups.

• The quality of data varies across groups.

• The defined classification task is an oversimplification.
• It arises when a one-size-fit-all model is used for groups
with different conditional distributions. 

Aggregation Bias
Evaluation Bias
• It occurs when the evaluation and/or benchmark data for
an algorithm doesn’t represent the target population.
Buolamwini, Joy, and Timnit Gebru. "Gender shades: Intersectional accuracy disparities in
commercial gender classification." Conference on Fairness, Accountability and Transparency.
2018.
Formalizations and Mitigations
• A data generation and ML pipeline viewed as a series of
mapping functions.
Mitigating Aggregation Bias:
• adjusting g

• change r or t for transforming
the data
Mitigating Evaluation Bias:
• redefine k

• adjusting X, Y
^ ^
Mitigating Representation Bias:
• improve s
Measurement and historical Bias:
• adjust s will likely be ineffective
?

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Lab presentation (a framework for understanding unintended consequences of machine learning)

  • 1. A Framework for Understanding Unintended consequences of Machine Learning Author: Harini Suresh (MIT), John V. Guttag(MIT) Presented: Chenguang Xu “Shine”
  • 2. The Problem with Biased data • Various unwanted consequences of ML algorithm arise in some way from biased data. • Bias refers to an unintended or potentially harmful property of the data. • Data is a product of many factors, and is the product of a process
  • 3. An illustrative Scenario Lack of data on women, introducing more data solved the issue. The use of a proxy label (human assessment of quality) versus the true label (actual qualification) allowed the model to discriminate by gender.
  • 4. Five Sources of Bias in ML
  • 5. Historical Bias It is a fundamental, structural issue with the very first step of the data generation process.
  • 6. Representation Bias • It arises when defining and sampling from a population. • It can arise for several reasons: • The sampling methods only reach a portion of the population. • The population of interest has changed or is distinct from the population used during model training.
  • 7. Representation Bias (cont.) Shankar, Shreya, et al. "No classification without representation: Assessing geodiversity issues in open data sets for the developing world." arXiv preprint arXiv:1711.08536 (2017).
  • 8. Representation Bias (cont.) Photos of bridegrooms from different countries aligned by the log-likelihood that the classifier trained on Open Images assigns to the bridegroom class. Shankar, Shreya, et al. "No classification without representation: Assessing geodiversity issues in open data sets for the developing world." arXiv preprint arXiv:1711.08536 (2017).
  • 9. Measurement Bias • It arises when subsequently choosing and measuring the particular features of interest. • It can arise in several ways: • The granularity of data varies across groups. • The quality of data varies across groups. • The defined classification task is an oversimplification.
  • 10. • It arises when a one-size-fit-all model is used for groups with different conditional distributions. Aggregation Bias
  • 11. Evaluation Bias • It occurs when the evaluation and/or benchmark data for an algorithm doesn’t represent the target population. Buolamwini, Joy, and Timnit Gebru. "Gender shades: Intersectional accuracy disparities in commercial gender classification." Conference on Fairness, Accountability and Transparency. 2018.
  • 12. Formalizations and Mitigations • A data generation and ML pipeline viewed as a series of mapping functions. Mitigating Aggregation Bias: • adjusting g • change r or t for transforming the data Mitigating Evaluation Bias: • redefine k • adjusting X, Y ^ ^ Mitigating Representation Bias: • improve s Measurement and historical Bias: • adjust s will likely be ineffective
  • 13.